How can classical multidimensional scaling go wrong?
Authors: Rishi Sonthalia, Greg Van Buskirk, Benjamin Raichel, Anna Gilbert
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we do two things. First, we empirically verify all of the theoretical claims. Second, we show that on the downstream task of classification, if we use c MDS to embed the data, then as the embedding dimension increases, the classification accuracy gets worse. Thus, suggesting that the embedding quality of c MDs degrades. [...] For the classification tasks, we switch to more standard benchmark datatsets: MNIST, Fashion MNIST, and CIFAR10. |
| Researcher Affiliation | Academia | Rishi Sonthalia University of Michigan rsonthal@umich.edu Gregory Van Buskirk University of Texas Dallas greg.vanbuskirk@utdallas.edu Benjamin Raichel University of Texas Dallas Benjamin.Raichel@utdallas.edu Anna C. Gilbert Yale University anna.gilbert@yale.edu |
| Pseudocode | Yes | Algorithm 1 Classical Multidimensional Scaling. [...] Algorithm 2 Lower Bound Algorithm. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, such as a specific repository link, an explicit code release statement, or code in supplementary materials. |
| Open Datasets | Yes | First, are metrics that come from graphs and for these we use Celegans Rossi and Ahmed [2015] and Portugal Rozemberczki et al. [2019] datasets. [...] For both of these metrics we use the heart dataset Detrano et al. [1989]. [...] For the classification tasks, we switch to more standard benchmark datatsets: MNIST, Fashion MNIST, and CIFAR10. |
| Dataset Splits | No | The paper specifies a train/test split ('first 1000 images are training points' and 'tested the network on the remaining 1000 points') but does not mention a separate validation set or other specific data partitioning details like exact percentages for a three-way split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper states 'trained a feed-forward 3 layer neural network' but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |